2004Poultry ScienceOpen access

Chicken single nucleotide polymorphism identification and selection for genetic mapping

Ruud Jalving, Ruben van’t Slot, Bernard A. van Oost

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Abstract

Single nucleotide polymorphisms (SNP) are the ideal markers for high-density genome wide mapping. A total of 327,000 expressed sequence tag (EST) sequences, obtained from the ChickEST project, were examined for the presence of SNP. A total of 32,268 potential chicken SNP were identified and stored in a customized Microsoft Access database and evaluated in silico for their usability for a high-density genetic map. Based on a minimum of 3 for the minor allele occurrence and a minimum of 30% for the minor allele frequency, 5,332 reliable SNP were selected, of which both SNP alleles were present in the database at a high frequency. To test the usefulness of the in silico SNP identification, 24 SNP affecting a BglII site were used for a genotyping study. A functional PCR assay could be designed for 21 of the 24 SNP. It was possible to validate 90% of this marker subset (21 SNP) by BglII restriction analysis. The high percentage of validated markers demonstrates the reliability of the 5,332 chicken SNP markers. Furthermore, the limited number of genomic DNA samples necessary to validate 90% of the SNP markers confirmed the prediction of the high frequency at which both alleles of the selected SNP were present in the tested chicken populations.

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Single nucleotide polymorphisms (SNP) are the ideal markers for high-density genome wide mapping. A total of 327,000 expressed sequence tag (EST) sequences, obtained from the ChickEST project, were examined for the presence of SNP. A total of 32,268 potential chicken SNP were identified and stored in a customized Microsoft Access database and evaluated in silico for their usability for a high-density genetic map. Based on a minimum of 3 for the minor allele occurrence and a minimum of 30% for the minor allele frequency, 5,332 reliable SNP were selected, of which both SNP alleles were present in the database at a high frequency. To test the usefulness of the in silico SNP identification, 24 SNP affecting a BglII site were used for a genotyping study. A functional PCR assay could be designed for 21 of the 24 SNP. It was possible to validate 90% of this marker subset (21 SNP) by BglII restriction analysis. The high percentage of validated markers demonstrates the reliability of the 5,332 chicken SNP markers. Furthermore, the limited number of genomic DNA samples necessary to validate 90% of the SNP markers confirmed the prediction of the high frequency at which both alleles of the selected SNP were present in the tested chicken populations.

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Available abstract

Single nucleotide polymorphisms (SNP) are the ideal markers for high-density genome wide mapping. A total of 327,000 expressed sequence tag (EST) sequences, obtained from the ChickEST project, were examined for the presence of SNP. A total of 32,268 potential chicken SNP were identified and stored in a customized Microsoft Access database and evaluated in silico for their usability for a high-density genetic map. Based on a minimum of 3 for the minor allele occurrence and a minimum of 30% for the minor allele frequency, 5,332 reliable SNP were selected, of which both SNP alleles were present in the database at a high frequency. To test the usefulness of the in silico SNP identification, 24 SNP affecting a BglII site were used for a genotyping study. A functional PCR assay could be designed for 21 of the 24 SNP. It was possible to validate 90% of this marker subset (21 SNP) by BglII restriction analysis. The high percentage of validated markers demonstrates the reliability of the 5,332 chicken SNP markers. Furthermore, the limited number of genomic DNA samples necessary to validate 90% of the SNP markers confirmed the prediction of the high frequency at which both alleles of the selected SNP were present in the tested chicken populations.

Key concepts: SNP, Molecular Inversion Probe, Single-nucleotide polymorphism, Minor allele frequency, Genetics, SNP genotyping, Genotyping, Biology

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